OxWearables/ssl-wearables
Self-supervised learning for wearables using the UK-Biobank (>700,000 person-days)
This tool helps researchers and health professionals develop advanced models to recognize human activities from wearable sensor data. It takes raw activity data from accelerometers or similar devices and outputs highly accurate classifications of activities like walking, running, or sleeping. Anyone working with health monitoring, fitness tracking, or behavioral science using wearables can benefit.
148 stars. No commits in the last 6 months.
Use this if you need to build a new human activity recognition system and want to leverage pre-trained models from large datasets to achieve high accuracy with less of your own labeled data.
Not ideal if you're looking for a ready-to-use application or a commercial solution, as this project requires programming knowledge to integrate and fine-tune.
Stars
148
Forks
40
Language
Jupyter Notebook
License
—
Category
Last pushed
Oct 24, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/OxWearables/ssl-wearables"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
OxWearables/stepcount
Improved Step Counting via Foundation Models for Wrist-Worn Accelerometers
OxWearables/actinet
An activity classification model based on self-supervised learning for wrist-worn accelerometer data.
aqibsaeed/Human-Activity-Recognition-using-CNN
Convolutional Neural Network for Human Activity Recognition in Tensorflow
felixchenfy/Realtime-Action-Recognition
Apply ML to the skeletons from OpenPose; 9 actions; multiple people. (WARNING: I'm sorry that...
guillaume-chevalier/LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM...